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Open Access Research

Transcriptomic analysis of genetically defined autism candidate genes reveals common mechanisms of action

Thomas A Lanz1, Edward Guilmette1, Mark M Gosink2, James E Fischer3, Lawrence W Fitzgerald15, Diane T Stephenson16 and Mathew T Pletcher4*

Author Affiliations

1 Neuroscience Research Unit, Pfizer Inc, Main Street, Cambridge, MA 02140, USA

2 Investigative Toxicology, Pfizer, Inc, Eastern Point Road, Groton, CT 06340, USA

3 Compound Safety Prediction, Pfizer, Inc, Eastern Point Road, Groton, CT 06340, USA

4 Rare Disease Research Unit, Pfizer, Inc, Cambridge Park Drive, Cambridge, MA 02140, USA

5 Current Address: LW Fitzgerald Consulting, LLC, 35 Plum Hill Rd., East Lyme, CT 06333, USA

6 Current Address: Critical Path Institute, East River Road, Tucson, AZ 85718, USA

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Molecular Autism 2013, 4:45  doi:10.1186/2040-2392-4-45

Published: 15 November 2013

Abstract

Background

Austism spectrum disorder (ASD) is a heterogeneous behavioral disorder or condition characterized by severe impairment of social engagement and the presence of repetitive activities. The molecular etiology of ASD is still largely unknown despite a strong genetic component. Part of the difficulty in turning genetics into disease mechanisms and potentially new therapeutics is the sheer number and diversity of the genes that have been associated with ASD and ASD symptoms. The goal of this work is to use shRNA-generated models of genetic defects proposed as causative for ASD to identify the common pathways that might explain how they produce a core clinical disability.

Methods

Transcript levels of Mecp2, Mef2a, Mef2d, Fmr1, Nlgn1, Nlgn3, Pten, and Shank3 were knocked-down in mouse primary neuron cultures using shRNA constructs. Whole genome expression analysis was conducted for each of the knockdown cultures as well as a mock-transduced culture and a culture exposed to a lentivirus expressing an anti-luciferase shRNA. Gene set enrichment and a causal reasoning engine was employed to identify pathway level perturbations generated by the transcript knockdown.

Results

Quantification of the shRNA targets confirmed the successful knockdown at the transcript and protein levels of at least 75% for each of the genes. After subtracting out potential artifacts caused by viral infection, gene set enrichment and causal reasoning engine analysis showed that a significant number of gene expression changes mapped to pathways associated with neurogenesis, long-term potentiation, and synaptic activity.

Conclusions

This work demonstrates that despite the complex genetic nature of ASD, there are common molecular mechanisms that connect many of the best established autism candidate genes. By identifying the key regulatory checkpoints in the interlinking transcriptional networks underlying autism, we are better able to discover the ideal points of intervention that provide the broadest efficacy across the diverse population of autism patients.